The state of Michigan sits on land stolen from multiple Indigenous communities: the Ojibwa, in the Upper Peninsula and northern Lower Peninsula; the Potawatomi, in the southern, eastern, and central parts of the state; and the Odawa, in the western Lower Peninsula. Known as the Council of the Three Fires, the three nations were primarily farming communities, raising corn, beans, squash, and more. These nations’ populations have been decimated due to the genocide of the Three Fires at the hands of European settlers. While small Ojibwa, Potawatomi, and Odawa communities continue to rebuild and thrive in the state of Michigan, it would be unacceptable to ignore the destruction and genocide of Native nations carried out by the creators of this state. As we discuss modern agriculture, those of us who are settlers remember that this land is not ours to sow.

Introduction

Coasts are ravaged by hurricanes, flooding, and wildfires. The central United States is plagued by drought, the South by heat waves. It is almost unanimously agreed in the scientific community that these drastic and deadly changes in climate are due to human-caused global warming (Cook, John, et al). In the agricultural cradle of the U.S. known as the Midwest, however, citizens are seeing the effects of climate change not only in the need for more deodorant as a result of hotter summers. Production of staple crops like corn, wheat, beans, and squash, along with the export and uses of water from the Great Lakes (tourism, fishing, and transport, among others), are all projected to falter or are already in the process of declining under the increasingly warm and unpredictable world. This blog examines and analyzes the impact of climate statistics and projections on the Michigan agricultural industry. We can conclude that the “Great Lakes State,” with its farmers, university students, fishermen, and urban dwellers, may be overwhelmed by the impacts of climate change on its agricultural, ecological, and economical success.

Data Retrieval and Analysis

Statistical support in the form of graphs for this hypothesis are based on data obtained from the National Oceanic and Atmospheric Administration (NOAA), which was collected at the University of Michigan in Ann Arbor. After retrieving such data from the NOAA, it was processed through the coding interface RStudio to create summaries and graphs of the data points. Close analysis of trends and patterns in this information, along with the review of other research done in the region, informed the conclusions which will be presented in this blog. It should be noted that we will be focusing on Ann Arbor data to paint a general picture of weather anomalies in the whole state of Michigan.

In the few months where the maximum temperature has not experienced a significant increase in average temperatures, the climate is still changing. The average minimum temperature of winters is rising quickly.

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "01", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -271.67 -125.81  -50.79  114.30  537.93 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4571.3516   912.4221  -5.010 1.93e-06 ***
## YEAR            2.4805     0.4654   5.329 4.80e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 178.8 on 118 degrees of freedom
## Multiple R-squared:  0.194,  Adjusted R-squared:  0.1872 
## F-statistic:  28.4 on 1 and 118 DF,  p-value: 4.797e-07

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "02", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -311.76 -139.84  -51.71   84.74 1428.87 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -2864.2570  1104.7695  -2.593  0.01073 * 
## YEAR            1.5951     0.5636   2.830  0.00547 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 216.5 on 118 degrees of freedom
## Multiple R-squared:  0.06357,    Adjusted R-squared:  0.05564 
## F-statistic: 8.011 on 1 and 118 DF,  p-value: 0.005468

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "03", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -197.08 -115.52  -32.52   69.05 1841.41 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -680.4305  1073.8526  -0.634    0.528
## YEAR           0.4366     0.5478   0.797    0.427
## 
## Residual standard error: 210.5 on 117 degrees of freedom
## Multiple R-squared:  0.005399,   Adjusted R-squared:  -0.003102 
## F-statistic: 0.6351 on 1 and 117 DF,  p-value: 0.4271

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "04", ])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -64.54 -43.76 -24.83  18.18 493.12 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -578.0000   386.6245  -1.495    0.138
## YEAR           0.3197     0.1970   1.623    0.107
## 
## Residual standard error: 72.18 on 111 degrees of freedom
## Multiple R-squared:  0.02317,    Adjusted R-squared:  0.01437 
## F-statistic: 2.633 on 1 and 111 DF,  p-value: 0.1075

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "05", ])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.952 -4.215 -1.596  1.024 79.602 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 221.39910   72.06979   3.072  0.00279 **
## YEAR         -0.11146    0.03664  -3.042  0.00305 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.78 on 93 degrees of freedom
## Multiple R-squared:  0.09051,    Adjusted R-squared:  0.08073 
## F-statistic: 9.255 on 1 and 93 DF,  p-value: 0.003053

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "06", ])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## YEAR               0          0      NA       NA
## 
## Residual standard error: 0 on 89 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 89 DF,  p-value: NA

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "07", ])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## YEAR               0          0      NA       NA
## 
## Residual standard error: 0 on 89 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 89 DF,  p-value: NA

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "08", ])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## YEAR               0          0      NA       NA
## 
## Residual standard error: 0 on 89 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 89 DF,  p-value: NA

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "09", ])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## YEAR               0          0      NA       NA
## 
## Residual standard error: 0 on 88 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 88 DF,  p-value: NA

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "10", ])
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.112 -4.056 -4.021 -3.977 67.929 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  1.32563   76.80182   0.017    0.986
## YEAR         0.00138    0.03906   0.035    0.972
## 
## Residual standard error: 12.04 on 99 degrees of freedom
## Multiple R-squared:  1.262e-05,  Adjusted R-squared:  -0.01009 
## F-statistic: 0.001249 on 1 and 99 DF,  p-value: 0.9719

## 
## Call:
## lm(formula = SNOW ~ YEAR, data = MonthlySNOWSum[MonthlySNOWSum$Month == 
##     "11", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -102.57  -63.44  -25.14   34.67  323.09 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -795.5301   437.7314  -1.817   0.0718 .
## YEAR           0.4468     0.2233   2.001   0.0478 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 83.8 on 115 degrees of freedom
## Multiple R-squared:  0.03364,    Adjusted R-squared:  0.02524 
## F-statistic: 4.004 on 1 and 115 DF,  p-value: 0.04775